{"id":"W2024616421","doi":"10.1002/atr.5670350205","title":"Operational objective functions in designing public transport routes","year":2001,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Operator (biology); Integer programming; Public transport; Computer science; Work (physics); Operations research; Mathematical optimization; Nonlinear programming; Linear programming; Philosophy of design; Management science; Nonlinear system; Transport engineering; Engineering; Mathematics; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005810002,0.001494506,0.0008647643,0.001632484,0.0004300612,0.002470017,0.001057173,0.001052503,0.002696834],"category_scores_gemma":[0.006771694,0.0004444685,0.0005843385,0.0009620264,0.001186198,0.001862954,0.001100858,0.001101498,0.0002699322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002131735,"about_ca_system_score_gemma":0.001806595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003668558,"about_ca_topic_score_gemma":0.002148662,"domain_scores_codex":[0.9966196,0.002320199,0.00008342272,0.0001491831,0.0005889014,0.0002386346],"domain_scores_gemma":[0.9976986,0.001392923,0.0002122812,0.00006857558,0.0005275132,0.0001001575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000240849,0.00003598667,0.0002068646,0.00005591043,0.00001597152,0.00002782615,0.00004086272,0.9165627,0.0004137215,0.07115395,0.0005430715,0.01091899],"study_design_scores_gemma":[0.000007607322,0.00003628143,0.0001047581,0.00003168726,0.00000931388,0.000008676614,0.00005917724,0.9778627,0.0003772907,0.0200744,0.001421416,0.000006616773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02738325,0.0003650478,0.9613903,0.0003120175,0.00003184728,0.00010311,0.00008307167,0.0000569557,0.0102745],"genre_scores_gemma":[0.8613941,0.0005041597,0.1340381,0.00006180382,0.00005487763,0.0004434636,0.0001829846,0.00009009252,0.003230383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005810002,"threshold_uncertainty_score":0.03072661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02060969918148386,"score_gpt":0.2850057747736366,"score_spread":0.2643960755921527,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}